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YeastNet: Deep Learning Semantic Segmentation for Budding Yeast Single Cell Analysis

YeastNet is a library for analysing live-cell fluorescence microscopy of budding yeast cells, written in Python3 using PyTorch 1.0.

Requirements

YeastNet works best using a conda environment and has been tested on a win-64 system and a x64 Ubuntu system.

Getting Started

  1. Open a conda shell and use the environment.yml file to build a new conda environment.

Linux: conda env create -f environment.yml

  1. Activate the new conda environment

conda activate ynet

  1. Download the model parameters and Images

  2. Navigate to the folder containing the BrightField Images and run the Tracking Pipeline

python track.py -f 'Z2' -p True

Details

Options for the trackingPipeline are available using the command:

python track.py --help

Bright Field Images by default should be in the path "./Images/BF/." GFP Images should by default should be in the path "./Images/GFP/."

This library will use CUDA-based GPU computation for neural net inference by default. If no supported GPU is available, inference will complete using the CPU.

Files to Download

You can download the Images with Ground Truth Masks at: https://drive.google.com/open?id=17fEUZdODqfi0GwK9OQi3W3oKPX3vJHua

You can download a set of YeastNet2 parameters at: https://drive.google.com/open?id=10nGfO99JcNNtZXyWDtaQN_HmYaPQ6ga9

Export and place the YeastNet2 Model and the Images folder into the main directory.

Ground Truth Datasets

Segmentation Masks created for this work are available at:

https://drive.google.com/file/d/1DIFoyRt_uRiFw6DXfoG6coSAayfGwD_M/view?usp=sharing